REPOGEO REPORT · LITE

itayinbarr/little-coder

Default branch main · commit 2547bce7 · scanned 6/17/2026, 5:32:02 PM

GitHub: 1,554 stars · 97 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface itayinbarr/little-coder, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README H1 to specify its role as a harness for small LLMs

Why:

CURRENT

# little-coder

**A coding agent tuned for small local models, built on top of pi.**

COPY-PASTE FIX

# little-coder

**A specialized coding agent and benchmark harness designed to optimize and evaluate small, local language models for code generation.**

mediumtopics#2

Refine topics to emphasize "agent/harness for small LLMs" and avoid confusion with LLM models

Why:

CURRENT

ai-coding-assistant, aider-polygot, benchmark, code-generation, coding-agent, coding-agents, local-llm, ollama, qwen, small-language-models, terminal-bench, tool-use

COPY-PASTE FIX

ai-coding-assistant, aider-polygot, benchmark, code-generation, coding-agent, coding-agents, local-llm, terminal-bench, tool-use, llm-agent-framework, local-llm-harness, small-llm-optimization, code-generation-agent, llm-benchmarking

lowabout#3

Update the "About" description to align with the new README positioning

Why:

CURRENT

A harness optimized to smaller LLMs

COPY-PASTE FIX

A specialized coding agent and benchmark harness for optimizing and evaluating small, local language models.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface itayinbarr/little-coder

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Code Llama

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Code Llama · recommended 2×
DeepSeek Coder · recommended 2×
Mistral 7B · recommended 1×
Phind-CodeLlama-34B · recommended 1×
StarCoder · recommended 1×

CATEGORY QUERY
How can I leverage small, local language models for effective AI coding assistance?
you: not recommended
AI recommended (in order):
1. Code Llama
2. Mistral 7B
3. Phind-CodeLlama-34B
4. StarCoder
5. DeepSeek Coder
6. llama.cpp
7. LoRA
8. VS Code extensions
9. Ollama
10. Text Generation WebUI
AI recommended 10 alternatives but never named itayinbarr/little-coder. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Which coding agents provide strong performance for code generation using local small LLMs?
you: not recommended
AI recommended (in order):
1. Continue (continue-team/continue)
2. OpenDevin (OpenDevin/OpenDevin)
3. Smol-Developer (smol-ai/smol-developer)
4. Auto-GPT (Significant-Gravitas/Auto-GPT)
5. GPT-Engineer (gpt-engineer-org/gpt-engineer)
6. Code Llama
7. DeepSeek Coder
8. Ollama (ollama/ollama)
9. LM Studio
10. vLLM (vllm-project/vllm)
AI recommended 10 alternatives but never named itayinbarr/little-coder. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of itayinbarr/little-coder?
pass
AI did not name itayinbarr/little-coder — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts itayinbarr/little-coder in production, what risks or prerequisites should they evaluate first?
pass
AI named itayinbarr/little-coder explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo itayinbarr/little-coder solve, and who is the primary audience?
pass
AI named itayinbarr/little-coder explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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HTML

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itayinbarr/little-coder — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite